Natural and Socioeconomic Factors and Their Interactive Effects on House Collapse Caused by Typhoon Mangkhut

Abstract

Typhoons are an environmental threat that mainly affects coastal regions worldwide. The interactive effects of natural and socioeconomic factors on the losses caused by typhoon disasters need further examination. In this study, GeoDetector was used to quantify the determinant powers of natural and socioeconomic factors and their interactive effects on the rate of house collapse in Guangdong and Guangxi Provinces of southeast China caused by Typhoon Mangkhut in 2018. We further identify the dominant factors that influenced the disaster losses. The local indicators of spatial association method was then introduced to explain the spatial heterogeneity of the disaster losses under the influence of the dominant factor. The results indicate that both natural and socioeconomic factors significantly affected the house collapse rate. The maximum precipitation was the dominant factor, with a q value of 0.21, followed by slope and elevation, with q values of 0.17 and 0.13, respectively. Population density and per capita gross domestic product had q values of 0.15 and 0.13, respectively. Among all of the interactive effects of the influencing factors, the interactive effect of elevation and the ratio of brick-wood houses had the greatest influence (q = 0.63) on the house collapse rate. These results can contribute to the formulation of more specific safety and property protection policies.

Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

“…It was assumed that if a given factor (X) determined the response variable (Y), the response variable would present a similar spatial distribution to that of the factor [ 39 , 40 , 41 ]. It has been employed in the fields of geology, environment, health, disasters, and other many fields [ 42 , 43 , 44 ], as indicated in the following formula: where the q value ranged from 0 to 1, indicating the determinant power of the factor. The larger the value of q , the stronger the determinant power of the factor.…”

“…It was assumed that if a given factor (X) determined the response variable (Y), the response variable would present a similar spatial distribution to that of the factor [39][40][41]. It has been employed in the fields of geology, environment, health, disasters, and other many fields [42][43][44], as indicated in the following formula:…”

Spatial pattern of the population casualty rate caused by super typhoon Lekima and quantification of the interactive effects of potential impact factors

“…Considering hazard factors, sensibility of disaster environment, vulnerability of hazard-affected bodies, and ability of disaster prevention and reduction, results from previous studies [ 13 , 18 , 25 , 26 ] and data availability, 12 natural and socioeconomic variables were selected in this study. Data collected during the same period from governmental statistic yearbooks of Zhejiang, Jiangsu, Shandong, and Liaoning Provinces (Fig.…”

Geospatial modeling of the tropical cyclone risk in the Guangdong Province, China

“…Because there is no consensus on the selection criterion for the conditioning factor used for TC risk assessment, 19 relative evaluation indicators under three risk component categories (hazard, vulnerability, and mitigation capacity) were examined based on a literature review (Hoque et al 2019;Nguyen et al 2019;Sajjad et al 2020;Zhang et al 2021). These risk criteria layers were obtained via data processing using projection, interpolation, clipping, distance analysis, and overlay analysis in ArcGIS.…”